A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

Abstract

In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

An optimized multiobjective CPU job scheduling using evolutionary algorithms

“…The attractiveness of a firefly (beta = 1) is kept at a high level and the light absorption coefficient (gamma = 0.1) is kept at a low level in order to promote more movement among the fireflies and also to speed up the process. The simulation parameter values for the ABC algorithm are taken based on [9]. With θ = 0.1, 0.2, and 0.3 three distribution patterns are considered for generating the random values for processing times: normal distribution, uniform distribution, and exponential distribution.…”

Multi-Objective for a Partial Flexible Open Shop Scheduling Problem Using a Priority-Based Evolutionary Algorithm

“…Rahmati et al [67] developed non-dominated sorting of EA and non dominated ranking EA for multi-objective PFOSP and he proposed new multi-objective Pareto-based modules and a new measure for the multi-objective evaluation. [42] 2002 FOSP EA + AL Baykasoglu et al [7] 2004 FOSP TS + PDR Xia and Wu [79] 2005 FOSP PSO + SA Gao et al [26] 2006 FOSP EA Gao et al [27] 2007 FOSP EA + BSP Zribi et al [89] 2007 FOSP EA + BBA + LS Gao et al [28] 2008 FOSP EA + VNS Tay and Ho [75] 2008 FOSP EA + PDR Wang et al [76] 2008 FOSP FBS + PDR Zhang et al [87] 2009 FOSP PSO + TS Li et al [50] 2010 FOSP EA + VNS Frutos et al [25] 2010 FOSP EA + SA Wang et al [77] 2010 FOSP EA + AIS Gao et al [30] 2010 FOSP EA + AIS Grobler et al [35] 2010 FOSP PSO + PDR Li et al [48] 2010 FOSP TS + VNS Moradi et al [58] 2011 FOSP EA + PDR Moslehi and Mahnam [59] 2011 FOSP PSO + LS Li et al [49] 2011 FOSP PSO Li et al [47] 2011 FOSP PSO Rajkumar et al [68] 2011 FOSP GRASP Chiang and Lin [17] 2013 FOSP EA Rahmati et al [67] 2013 FOSP Gas Shao et al [72] 2013 FOSP PSO + SA Gao et al [29] 2014 FOSP HSA + LS Jia and Hu [41] 2014 FOSP TS Karthikeyan et al [45] 2014 FOSP DFA + LS Li et al [51] 2014 FOSP PSO + TS Rohaninejad et al [69] 2015 FOSP EA Yuan and Xu [84] 2015 FOSP EA + LS Rohaninejad et al [69] proposed a nonlinear IP model and also the hybridized EA with meta-heuristic, which is a multi-attribute decision making method, for multi-objective PFOSP with machines capacity constraints. The computational results are obtained by well-known multi objective algorithms from the literature showed that the proposed algorithm to obtain throughout better performance, especially in the closeness of the solutions result to the Pareto optimal front.…”

“…Shao et al [72] developed a hybrid methodology, which uses the discrete particle swarm optimization for their global search and simulated annealing for local search and for multi-objective PFOSP. Li et al [47] and Li et al [49] proposed a hybridized artificial bee colony algorithm, which is a novel particle swarm methodology, for solving the multi-objective PFOSP. Li et al [51] combined artificial bee colony algorithm with tabu search for the multi-objective PFOSP with maintenance activities.…”

Hybridization of Evolutionary and Swarm Intelligence Techniques for Job Scheduling Problem

“…Inspired by the intelligent foraging behaviours of honeybee swarm, an artificial bee colony algorithm is developed, which is a new population-based meta-heuristic approach [8] [9]. In ABC algorithm, there are three kinds of foraging bees: employed bees, onlooker bees, and scout bees.…”